MetaWise: Combined Feature Selection and Weighting Method to Link the Serum Metabolome to Treatment Response and Survival in Glioblastoma.

Glioblastoma (GBM) is a highly malignant and devastating brain cancer characterized by its ability to rapidly and aggressively grow, infiltrating brain tissue, with nearly universal recurrence after the standard of care (SOC), which comprises maximal safe resection followed by chemoirradiation (CRT). The metabolic triggers leading to the reprogramming of tumor behavior and resistance are an area increasingly studied in relation to the tumor molecular features associated with outcome. There are currently no metabolomic biomarkers for GBM. Studying the metabolomic alterations in GBM patients undergoing CRT could uncover the biochemical pathways involved in tumor response and resistance, leading to the identification of novel biomarkers and the optimization of the treatment response. The feature selection process identifies key factors to improve the model's accuracy and interpretability. This study utilizes a combined feature selection approach, incorporating both Least Absolute Shrinkage and Selection Operator (LASSO) and Minimum Redundancy-Maximum Relevance (mRMR), alongside a rank-based weighting method (i.e., MetaWise) to link metabolomic biomarkers to CRT and the 12-month and 20-month overall survival (OS) status in patients with GBM. Our method shows promising results, reducing feature dimensionality when employed on serum-based large-scale metabolomic datasets (University of Florida) for all our analyses. The proposed method successfully identified a set of eleven serum biomarkers shared among three datasets. The computational results show that the utilized method achieves 96.711%, 92.093%, and 86.910% accuracy rates with 48, 46, and 33 selected features for the CRT, 12-month, and 20-month OS-based metabolomic datasets, respectively. This discovery has implications for developing personalized treatment plans and improving patient outcomes.

MGMT ProFWise: Unlocking a New Application for Combined Feature Selection and the Rank-Based Weighting Method to Link MGMT Methylation Status to Serum Protein Expression in Patients with Glioblastoma.

Glioblastoma (GBM) is a fatal brain tumor with limited treatment options. O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation status is the central molecular biomarker linked to both the response to temozolomide, the standard chemotherapy drug employed for GBM, and to patient survival. However, MGMT status is captured on tumor tissue which, given the difficulty in acquisition, limits the use of this molecular feature for treatment monitoring. MGMT protein expression levels may offer additional insights into the mechanistic understanding of MGMT but, currently, they correlate poorly to promoter methylation. The difficulty of acquiring tumor tissue for MGMT testing drives the need for non-invasive methods to predict MGMT status. Feature selection aims to identify the most informative features to build accurate and interpretable prediction models. This study explores the new application of a combined feature selection (i.e., LASSO and mRMR) and the rank-based weighting method (i.e., MGMT ProFWise) to non-invasively link MGMT promoter methylation status and serum protein expression in patients with GBM. Our method provides promising results, reducing dimensionality (by more than 95%) when employed on two large-scale proteomic datasets (7k SomaScan® panel and CPTAC) for all our analyses. The computational results indicate that the proposed approach provides 14 shared serum biomarkers that may be helpful for diagnostic, prognostic, and/or predictive operations for GBM-related processes, given further validation.

The value of the wireless stethoscope in patients with COVID-19 infection in a makeshift hospital.

OBJECTIVE: When COVID-19 sweeps the world, traditional stethoscopes are seen as infectious agents and then the use of stethoscopes is limited especially when health providers were in their personal protective equipment. These reasons led to the ignoring of the values of stethoscopes during pandemics. This study aims to explore the value of wireless stethoscopes in patients of a makeshift hospital. MATERIAL AND METHODS: 200 consecutive hospitalized patients with confirmed SARS-CoV-2 at Lingang Makeshift Hospital in Shanghai, China, were enrolled from April 10 to May 10, 2022 (Trial Registration Number: ChiCTR2000038272,2020/9/15). They were randomly divided into two groups. In group A (n = 100), patients were examined without a stethoscope. In group B (n = 100), lung breath sounds and heart sounds were examined with a wireless stethoscope, and positive signs were recorded. The duration of cough and tachycardia symptoms, as well as emergency cases, were compared between the two groups. In addition, the pressure, anxiety, and depression of patients in the two groups were investigated using the DAS-21 questionnaire scale, to observe the psychological impact of the stethoscope-based doctor-patient communication on patients in the makeshift hospital. RESULTS: There was no significant difference in baseline characteristics between the two groups. In group B, some significant positive signs were detected by wireless stethoscopes, including pulmonary rales and tachycardia, etc. Moreover, the therapeutic measures based on these positive signs effectively alleviated the symptoms of cough and tachycardia, which showed that the duration of symptoms was significantly shorter than that of group A (cough: 2.8 ± 0.9 vs. 3.6 ± 0.9; palpitation: 1.4 ± 0.7 vs. 2.6 ± 0.7). In particular, the number of emergency cases in group B is less than that in group A (1% vs. 3%), and the severity is lower. Notably, stethoscope-based doctor-patient communication was found to be effective in alleviating psychological measures of group B patients. CONCLUSION: Wireless stethoscopes in makeshift hospitals can avoid cross-infections and detect more valuable positive signs, which can help health providers make accurate decisions and relieve patients' symptoms more quickly. Moreover, stethoscope-based doctor-patient communication can diminish the psychological impacts of the epidemic on isolated patients in makeshift hospitals. Trial registration This study was registered in the Chinese Clinical Trial (ChiCTR2000038272) at http://www.chictr.org.cn . http://www.chinadrugtrials.org.cn/clinicaltrials.searchlistdetail.dhtml .

Hierarchical Voting-Based Feature Selection and Ensemble Learning Model Scheme for Glioma Grading with Clinical and Molecular Characteristics.

Determining the aggressiveness of gliomas, termed grading, is a critical step toward treatment optimization to increase the survival rate and decrease treatment toxicity for patients. Streamlined grading using molecular information has the potential to facilitate decision making in the clinic and aid in treatment planning. In recent years, molecular markers have increasingly gained importance in the classification of tumors. In this study, we propose a novel hierarchical voting-based methodology for improving the performance results of the feature selection stage and machine learning models for glioma grading with clinical and molecular predictors. To identify the best scheme for the given soft-voting-based ensemble learning model selections, we utilized publicly available TCGA and CGGA datasets and employed four dimensionality reduction methods to carry out a voting-based ensemble feature selection and five supervised models, with a total of sixteen combination sets. We also compared our proposed feature selection method with the LASSO feature selection method in isolation. The computational results indicate that the proposed method achieves 87.606% and 79.668% accuracy rates on TCGA and CGGA datasets, respectively, outperforming the LASSO feature selection method.

Ubiquitin-Specific Protease 4 Is an Endogenous Negative Regulator of Metabolic Dysfunctions in Nonalcoholic Fatty Liver Disease in Mice.

Nonalcoholic fatty liver disease (NAFLD), characterized by hepatic steatosis (HS), insulin resistance (IR), and inflammation, poses a high risk of cardiometabolic disorders. Ubiquitin specific protease 4 (USP4), a deubiquitinating enzyme, is pivotally involved in regulating multiple inflammatory pathways; however, the role of USP4 in NAFLD is unknown. Here, we report that USP4 expression was dramatically down-regulated in livers from NAFLD patients and different NAFLD mouse models induced by high-fat diet (HFD) or genetic deficiency (ob/ob) as well as in palmitate-treated hepatocytes. Hepatocyte-specific USP4 depletion exacerbated HS, IR, and inflammatory response in HFD-induced NAFLD mice. Conversely, hepatic USP4 overexpression notably alleviated the pathological alterations in two different NAFLD models. Mechanistically, hepatocyte USP4 directly bound to and deubiquitinated transforming growth factor-ß activated kinase 1 (TAK1), leading to a suppression of the activation of downstream nuclear factor kappa B (NF-κB) and c-Jun N-terminal kinase (JNK) cascades, which, in turn, reversed the disruption of insulin receptor substrate/protein kinase B/glycogen synthase kinase 3 beta (IRS-AKT-GSK3ß) signaling. In addition, USP4-TAK1 interaction and subsequent TAK1 deubiquitination were required for amelioration of metabolic dysfunctions. Conclusion: Collectively, the present study provides evidence that USP4 functions as a pivotal suppressor in NAFLD and related metabolic disorders. (Hepatology 2018; 00:000-000).

The effect of estrogen on diabetic wound healing is mediated through increasing the function of various bone marrow-derived progenitor cells.

OBJECTIVE: Endothelial progenitor cells (EPCs) are the key cells of postnatal neovascularization, and mesenchymal stem cells (MSCs) possess pluripotent differentiation capacity and contribute to tissue regeneration and wound healing. Both EPCs and MSCs are critical to the wound repair process, which is hindered in diabetes mellitus. Diabetes has been shown to decrease the function of these progenitor cells, whereas estrogen has beneficial wound healing effects. However, the role of estrogen in modulating EPC and MSC biology in diabetes is unknown. We investigated the effect of estrogen on improving bone marrow (BM)-derived EPC and MSC function using a murine diabetic wound healing model. METHODS: Female diabetic db+/db+ and nondiabetic control mice were wounded cutaneously and treated with topical estrogen or placebo cream. On day 5 after wounding, BM cells were harvested to quantify EPC number and colony-forming units of EPCs and MSCs. Wound healing rate was concurrently studied. Vessel density and scar density were then quantified using whole body perfusion and laser confocal microscopy. EPC recruitment was documented by immunohistochemistry to identify CD34- and vascular endothelial growth factor receptor 2-positive cells in the vessel wall. Data were analyzed by analysis of variance. RESULTS: Topical estrogen significantly increased colony-forming units of both EPCs and MSCs compared with placebo treatment, indicating improved viability and proliferative ability of these cells. Consistently, increased recruitment of EPCs to diabetic wounds and higher vessel density were observed in estrogen-treated compared with placebo-treated mice. Consequently, topical estrogen significantly accelerated wound healing as early as day 6 after wounding. In addition, scar density resulting from collagen deposition was increased in the estrogen-treated group, reflecting increased MSC activity and differentiation. CONCLUSIONS: Estrogen treatment increases wound healing and wound neovascularization in diabetic mice. Our data implicate that these beneficial effects may be mediated through improving the function of BM-derived EPCs and MSCs.

Output factor comparison of Monte Carlo and measurement for Varian TrueBeam 6 MV and 10 MV flattening filter-free stereotactic radiosurgery system.

The dose measurements of the small field sizes, such as conical collimators used in stereotactic radiosurgery (SRS), are a significant challenge due to many factors including source occlusion, detector size limitation, and lack of lateral electronic equilibrium. One useful tool in dealing with the small field effect is Monte Carlo (MC) simulation. In this study, we report a comparison of Monte Carlo simulations and measurements of output factors for the Varian SRS system with conical collimators for energies of 6 MV flattening filter-free (6 MV) and 10 MV flattening filter-free (10 MV) on the TrueBeam accelerator. Monte Carlo simulations of Varian's SRS system for 6 MV and 10 MV photon energies with cones sizes of 17.5 mm, 15.0 mm, 12.5 mm, 10.0 mm, 7.5 mm, 5.0 mm, and 4.0 mm were performed using EGSnrc (release V4 2.4.0) codes. Varian's version-2 phase-space files for 6 MV and 10 MV of TrueBeam accelerator were utilized in the Monte Carlo simulations. Two small diode detectors Edge (Sun Nuclear) and Small Field Detector (SFD) (IBA Dosimetry) were applied to measure the output factors. Significant errors may result if detector correction factors are not applied to small field dosimetric measurements. Although it lacked the machine-specific kfclin,fmsrQclin,Qmsr correction factors for diode detectors in this study, correction factors were applied utilizing published studies conducted under similar conditions. For cone diameters greater than or equal to 12.5 mm, the differences between output factors for the Edge detector, SFD detector, and MC simulations are within 3.0% for both energies. For cone diameters below 12.5 mm, output factors differences exhibit greater variations.

Reactive oxygen species responsive double-locked liposome collaborative photodynamic therapy for reducing electrical conduction recurrence after radiofrequency catheter ablation.

Radiofrequency catheter ablation (RFCA) is the preferred technique for the treatment of atrial fibrillation, but the recovery of electrical conduction after ablation seriously endangers the health of patients. This study aimed to develop reactive oxygen species (ROS) responsive double-locked liposome collaborative photodynamic therapy (PDT) to target the ablation area and reduce the recovery of electrical conduction after ablation. The successful synthesis of ß-cyclodextrin modified with phenylboronic acid pinacol ester (OCD) was confirmed by 1H NMR and FT-IR. Furthermore, the successful synthesis of octadecylamine-modified indocyanine green (ICG-ODA) was confirmed by 1H NMR and mass spectrometry. The ICG-ODA was encapsulated in liposomes to generate a double-locked hybrid liposome (ICG-ODA@rNP), which was subsequently characterized. Several properties of ICG-ODA@rNP were evaluated, including the drug release, targeting ability and ability to inhibit electrical conduction recurrence. Moreover, a model was constructed for the blockage of electrical conduction after RFCA in rabbits to further evaluate ICG-ODA@rNP. The preliminary safety evaluation of ICG-ODA@rNP was also performed. The ICG-ODA@rNP with a uniform particle size showed excellent storage stability. The nanoparticle can sensitively release drugs under ROS environment, and exhibits excellent photothermal effects. Furthermore, ICG-ODA@rNP can circulate for a long time in vivo and accumulate significantly in the ablation area. In a pacing test with a left atrial appendage (LAA), these nanoparticles, combined with PDT, reduced the ratio of electrical conduction recovery, which was confirmed by a hematoxylin and eosin (H&E) test. Further molecular analysis revealed that ICG-ODA@rNP could increase RFCA-induced apoptosis and ROS levels. Specifically, ICG-ODA@rNP significantly increased the expression of Bax and cleaved caspase-3, and decreased the expression of Bcl-2. In addition, the excellent biosafety of the double-locked nanoparticle was verified. This study provides evidence that ICG-ODA@rNP, with the double lock characteristic and biosafety, which exhibits a targeting effect on RFCA-induced cardiac injury areas, which further reduce electrical conduction recovery in RFCA areas by collaborativing PDT.

The efficacy of coaxial percutaneous iodine-125 seed implantation combined with arterial infusion chemotherapy for advanced pancreatic cancer: a randomized clinical trial.

BACKGROUND: This study aimed to evaluate the clinical efficacy of coaxial percutaneous Iodine-125 (125I) seed implantation in combination with arterial infusion chemotherapy for the treatment of advanced pancreatic cancer (PC) through a randomized controlled trial. METHODS: A total of 101 patients with advanced PC were randomized into two groups: control group treated with systemic intravenous chemotherapy and experimental group that received 125I seed implantation in combination with arterial infusion chemotherapy. Outcomes, including tumor control, abdominal pain relief, and survival time were compared between these two groups (Trial Registration No. KYKT2018-65). RESULTS: Pretreatment abdominal pain scores were comparable between the two groups, whereas the abdominal pain scores at 1- and 3-month post-treatment were significantly lower in the control group than those in the experimental group (1-month: 3.74 ± 1.54 vs. 4.48 ± 1.46, p = .015; 3-month: 3.64 ± 2.21 vs. 5.40 ± 1.56, p < .001). At 3-month post-treatment, computed tomography (CT) scan revealed a significantly higher disease control rate in the experimental group than that in the control group (94.0% vs. 74.5%, p = .007). The median survival time in the experimental group was significantly longer than that in the control group (15-month vs. 9-month, p < .001). CONCLUSION: The combination of coaxial percutaneous 125I seed implantation with arterial infusion chemotherapy could significantly alleviate abdominal pain, improve tumor control rates, and prolong survival time in patients with advanced PC.

Diagnosing Progression in Glioblastoma-Tackling a Neuro-Oncology Problem Using Artificial-Intelligence-Derived Volumetric Change over Time on Magnetic Resonance Imaging to Examine Progression-Free Survival in Glioblastoma.

Glioblastoma (GBM) is the most aggressive and the most common primary brain tumor, defined by nearly uniform rapid progression despite the current standard of care involving maximal surgical resection followed by radiation therapy (RT) and temozolomide (TMZ) or concurrent chemoirradiation (CRT), with an overall survival (OS) of less than 30% at 2 years. The diagnosis of tumor progression in the clinic is based on clinical assessment and the interpretation of MRI of the brain using Response Assessment in Neuro-Oncology (RANO) criteria, which suffers from several limitations including a paucity of precise measures of progression. Given that imaging is the primary modality that generates the most quantitative data capable of capturing change over time in the standard of care for GBM, this renders it pivotal in optimizing and advancing response criteria, particularly given the lack of biomarkers in this space. In this study, we employed artificial intelligence (AI)-derived MRI volumetric parameters using the segmentation mask output of the nnU-Net to arrive at four classes (background, edema, non-contrast enhancing tumor (NET), and contrast-enhancing tumor (CET)) to determine if dynamic changes in AI volumes detected throughout therapy can be linked to PFS and clinical features. We identified associations between MR imaging AI-generated volumes and PFS independently of tumor location, MGMT methylation status, and the extent of resection while validating that CET and edema are the most linked to PFS with patient subpopulations separated by district rates of change throughout the disease. The current study provides valuable insights for risk stratification, future RT treatment planning, and treatment monitoring in neuro-oncology.

Correction: Tasci et al. RadWise: A Rank-Based Hybrid Feature Weighting and Selection Method for Proteomic Categorization of Chemoirradiation in Patients with Glioblastoma. Cancers 2023, 15, 2672.

In the original publication [...].

Serum CD133-Associated Proteins Identified by Machine Learning Are Connected to Neural Development, Cancer Pathways, and 12-Month Survival in Glioblastoma.

Glioma is the most prevalent type of primary central nervous system cancer, while glioblastoma (GBM) is its most aggressive variant, with a median survival of only 15 months when treated with maximal surgical resection followed by chemoradiation therapy (CRT). CD133 is a potentially significant GBM biomarker. However, current clinical biomarker studies rely on invasive tissue samples. These make prolonged data acquisition impossible, resulting in increased interest in the use of liquid biopsies. Our study, analyzed 7289 serum proteins from 109 patients with pathology-proven GBM obtained prior to CRT using the aptamer-based SOMAScan® proteomic assay technology. We developed a novel methodology that identified 24 proteins linked to both serum CD133 and 12-month overall survival (OS) through a multi-step machine learning (ML) analysis. These identified proteins were subsequently subjected to survival and clustering evaluations, categorizing patients into five risk groups that accurately predicted 12-month OS based on their protein profiles. Most of these proteins are involved in brain function, neural development, and/or cancer biology signaling, highlighting their significance and potential predictive value. Identifying these proteins provides a valuable foundation for future serum investigations as validation of clinically applicable GBM biomarkers can unlock immense potential for diagnostics and treatment monitoring.

The role of KNTC1 in the regulation of proliferation, migration and tumorigenesis in colorectal cancer.

BACKGROUND: Current findings have revealed that kinetochore-associated protein 1 (KNTC1) plays a pivotal role in the carcinogenesis of numerous types of cancer. This study was undertaken to inspect the role and probable underlying mechanisms of KNTC1 during the genesis and progression of colorectal cancer. METHODS: Immunohistochemistry was implemented to determine KNTC1 expression levels in colorectal cancer tissues and para-carcinoma tissues. The association between KNTC1 expression profiles and several clinicopathological traits of colorectal cancer cases was examined employing Mann-Whitney U, Spearman, and Kaplan-Meier analyses. To track the proliferation, apoptosis, cell cycle, migration and in vivo carcinogenesis of colorectal cancer cells, KNTC1 was knocked down in colorectal cell line via RNA interference. To investigate the potential mechanism, the expression profile alterations of associated proteins were detected using human apoptosis antibody arrays, and verified by Western blot analysis. RESULTS: In colorectal cancer tissues, KNTC1 was substantially expressed, and it was associated with the pathological grade as well as overall survival rate of the disease. The knockdown of KNTC1 was able to inhibit proliferation, cell cycle, migration and in vivo tumorigenesis of colorectal cancer cells, but promote apoptosis. CONCLUSIONS: KNTC1 is a key player in the emergence of colorectal cancer and may serve as an early diagnostic indicator of precancerous lesions.

GradWise: A Novel Application of a Rank-Based Weighted Hybrid Filter and Embedded Feature Selection Method for Glioma Grading with Clinical and Molecular Characteristics.

Glioma grading plays a pivotal role in guiding treatment decisions, predicting patient outcomes, facilitating clinical trial participation and research, and tailoring treatment strategies. Current glioma grading in the clinic is based on tissue acquired at the time of resection, with tumor aggressiveness assessed from tumor morphology and molecular features. The increased emphasis on molecular characteristics as a guide for management and prognosis estimation underscores is driven by the need for accurate and standardized grading systems that integrate molecular and clinical information in the grading process and carry the expectation of the exposure of molecular markers that go beyond prognosis to increase understanding of tumor biology as a means of identifying druggable targets. In this study, we introduce a novel application (GradWise) that combines rank-based weighted hybrid filter (i.e., mRMR) and embedded (i.e., LASSO) feature selection methods to enhance the performance of feature selection and machine learning models for glioma grading using both clinical and molecular predictors. We utilized publicly available TCGA from the UCI ML Repository and CGGA datasets to identify the most effective scheme that allows for the selection of the minimum number of features with their names. Two popular feature selection methods with a rank-based weighting procedure were employed to conduct comprehensive experiments with the five supervised models. The computational results demonstrate that our proposed method achieves an accuracy rate of 87.007% with 13 features and an accuracy rate of 80.412% with five features on the TCGA and CGGA datasets, respectively. We also obtained four shared biomarkers for the glioma grading that emerged in both datasets and can be employed with transferable value to other datasets and data-based outcome analyses. These findings are a significant step toward highlighting the effectiveness of our approach by offering pioneering results with novel markers with prospects for understanding and targeting the biologic mechanisms of glioma progression to improve patient outcomes.

Identifying patients suitable for targeted adjuvant therapy: advances in the field of developing biomarkers for tumor recurrence following irradiation.

Introduction: Radiation therapy (RT) is commonly used to treat cancer in conjunction with chemotherapy, immunotherapy, and targeted therapies. Despite the effectiveness of RT, tumor recurrence due to treatment resistance still lead to treatment failure. RT-specific biomarkers are currently lacking and remain challenging to investigate with existing data since, for many common malignancies, standard of care (SOC) paradigms involve the administration of RT in conjunction with other agents. Areas Covered: Established clinically relevant biomarkers are used in surveillance, as prognostic indicators, and sometimes for treatment planning; however, the inability to intercept early recurrence or predict upfront resistance to treatment remains a significant challenge that limits the selection of patients for adjuvant therapy. We discuss attempts at intercepting early failure. We examine biomarkers that have made it into the clinic where they are used for treatment monitoring and management alteration, and novel biomarkers that lead the field with targeted adjuvant therapy seeking to harness these. Expert Opinion: Given the growth of data correlating interventions with omic analysis toward identifying biomarkers of radiation resistance, more robust markers of recurrence that link to biology will increasingly be leveraged toward targeted adjuvant therapy to make a successful transition to the clinic in the coming years.

Canine model of electrical conduction recurrence after radiofrequency catheter ablation constructed by CARTO3 and preliminary application evaluation of DOX-L.

BACKGROUND: Radiofrequency catheter ablation (RFCA) is widely used to treat arrhythmias. However, for atrial fibrillation, the recurrence rate after RFCA is still high. The development of an animal model that mimics the recurrence of electrical conduction after ablation is essential before we can explore the mechanisms involved or develop new therapeutic strategies. METHODS: Eighteen beagles aged 12 to 24 months were randomly assigned to this study. RFCA ablation of the right atrial free wall was performed. Then, electrical block and conduction recovery in the ablation area were evaluated using voltage mapping and pacing tests assisted by CARTO3 system. Finally, liposome doxorubicin (DOX-L) was intravenously injected after ablation to investigate the effect of DOX-L on this animal model. RESULTS: The conduction block (CB) rates at 5 min after ablation were 16.7%, 83.3%, and 100%, corresponding to 30w, 35w, and 40w power, respectively. However, after 20 min, the rate of CB was 0%, 33.3%, and 75%; thus, the combined success rate of CB and conduction recurrence was 16.7%, 50%, and 25%, respectively. The optimal ablation parameter is 35 W for 20 s, based on the CB rate, REC rate. After 10 days of ablation, the residual conduction recurrence rate was as high as 83.3% in the RFCA alone group, whereas there was no recurrence with RFCA combined with DOX-L treatment. CONCLUSIONS: The novel model accurately simulated the electrical conduction recurrence after cardiac radiofrequency ablation. RFCA combined with DOX-L treatment dramatically reduces the recurrence rate of electrical conduction after ablation.

pH-Sensitive Nanoparticles for Colonic Delivery Anti-miR-301a in Mouse Models of Inflammatory Bowel Diseases.

Though the anti-miR-301a (anti-miR) is a promising treatment strategy for inflammatory bowel disease (IBD), the degradability and the poor targeting of the intestine are a familiar issue. This study aimed to develop a multifunctional oral nanoparticle delivery system loaded with anti-miR for improving the targeting ability and the therapeutic efficacy. The HA-CS/ES100/PLGA nanoparticles (HCeP NPs) were prepared using poly (lactic-co-glycolic acid) copolymer (PLGA), enteric material Eudragit®S100 (ES100), chitosan (CS), and hyaluronic acid (HA). The toxicity of nanoparticles was investigated via the Cell Counting Kit-8, and the cellular uptake and inflammatory factors of nanoparticles were further studied. Moreover, we documented the colon targeting and pharmacodynamic properties of nanoparticles. The nanoparticles with uniform particle size exhibited pH-sensitive release, favorable gene protection, and storage stability. Cytology experiments showed that anti-miR@HCeP NPs improved the cellular uptake through HA and reduced pro-inflammatory factors. Administering anti-miR@HCeP NPs orally to IBD mice markedly reduced their pro-inflammatory factors levels and disease activity indices. We also confirmed that anti-miR@HCeP NPs mostly accumulated in the colon site, and effectively repaired the intestinal barrier, as well as relieved intestinal inflammation. The above nanoparticle is a candidate of the treatment for IBD due to its anti-inflammatory properties.

The serum soluble scavenger with 5 domains levels: A novel biomarker for individuals with heart failure.

Background: We aimed to explore the relationship between the serum Soluble Scavenger with 5 Domains (SSC5D) levels and heart failure (HF). Methods and Results: We retrospectively enrolled 276 patients diagnosed with HF or normal during hospitalization in Shanghai General Hospital between September 2020 and December 2021. Previously published RNA sequencing data were re-analyzed to confirm the expression profile of SSC5D in failing and non-failing human and mouse heart tissues. Quantitative real-time polymerase chain reaction assay was used to quantify Ssc5d mRNA levels in murine heart tissue after myocardial infarction and transverse aortic constriction surgery. To understand the HF-induced secreted proteins profile, 1,755 secreted proteins were investigated using human dilated cardiomyopathy RNA-seq data, and the results indicated that SSC5D levels were significantly elevated in failing hearts compared to the non-failing. Using single-cell RNA sequencing data, we demonstrated that Ssc5d is predominantly expressed in cardiac fibroblasts. In a murine model of myocardial infarction or transverse aortic constriction, Ssc5d mRNA levels were markedly increased compared with those in the sham group. Similarly, serum SSC5D levels were considerably elevated in the HF group compared with the control group [15,789.35 (10,745.32-23,110.65) pg/mL, 95% CI (16,263.01-19,655.43) vs. 8,938.72 (6,154.97-12,778.81) pg/mL, 95% CI (9,337.50-11,142.93); p < 0.0001]. Moreover, serum SSC5D levels were positively correlated with N-terminal pro-B-type natriuretic peptide (R = 0.4, p = 7.9e-12) and inversely correlated with left ventricular ejection fraction (R = -0.46, p = 9.8e-16). Conclusion: We concluded that SSC5D was a specific response to HF. Serum SSC5D may function as a novel biomarker and therapeutic target for patients with HF.

RadWise: A Rank-Based Hybrid Feature Weighting and Selection Method for Proteomic Categorization of Chemoirradiation in Patients with Glioblastoma.

Glioblastomas (GBM) are rapidly growing, aggressive, nearly uniformly fatal, and the most common primary type of brain cancer. They exhibit significant heterogeneity and resistance to treatment, limiting the ability to analyze dynamic biological behavior that drives response and resistance, which are central to advancing outcomes in glioblastoma. Analysis of the proteome aimed at signal change over time provides a potential opportunity for non-invasive classification and examination of the response to treatment by identifying protein biomarkers associated with interventions. However, data acquired using large proteomic panels must be more intuitively interpretable, requiring computational analysis to identify trends. Machine learning is increasingly employed, however, it requires feature selection which has a critical and considerable effect on machine learning problems when applied to large-scale data to reduce the number of parameters, improve generalization, and find essential predictors. In this study, using 7k proteomic data generated from the analysis of serum obtained from 82 patients with GBM pre- and post-completion of concurrent chemoirradiation (CRT), we aimed to select the most discriminative proteomic features that define proteomic alteration that is the result of administering CRT. Thus, we present a novel rank-based feature weighting method (RadWise) to identify relevant proteomic parameters using two popular feature selection methods, least absolute shrinkage and selection operator (LASSO) and the minimum redundancy maximum relevance (mRMR). The computational results show that the proposed method yields outstanding results with very few selected proteomic features, with higher accuracy rate performance than methods that do not employ a feature selection process. While the computational method identified several proteomic signals identical to the clinical intuitive (heuristic approach), several heuristically identified proteomic signals were not selected while other novel proteomic biomarkers not selected with the heuristic approach that carry biological prognostic relevance in GBM only emerged with the novel method. The computational results show that the proposed method yields promising results, reducing 7k proteomic data to 7 selected proteomic features with a performance value of 93.921%, comparing favorably with techniques that do not employ feature selection.